Exploring thermostat override behavior during direct load control events
Bibliographic record
Abstract
Abstract Direct load control (DLC) is considered a viable solution to promote demand-side energy management, in which the utility provider adjusts consumers’ temperature setpoints via smart thermostats. Users commonly have the option to interrupt DLC and override them by adjusting their thermostat setpoints. However, the occurrence of overrides can have a detrimental impact on the overall efficacy of DLC. The user discomfort and the fact that an override may increase the load unexpectedly on the grid highlight the importance of understanding override mechanisms during DLC and the uncertainty related to occupants’ responses. This study examined user interactions with smart thermostats during DLC events using real-world data from the Ecobee Donate Your Data (DYD) program. According to the results, 35% of DLC was overridden by users, resulting in higher energy consumption during peak periods. A comprehensive analysis of four types of variables was conducted. A decision tree algorithm was used to classify smart thermostat users into two categories: “compliant users,” who rarely override DLC, and “non-compliant users,” who frequently override DLC, based on general information about their behavior and preferences and without any prior DLC experience. Moreover, three distinct types of DLC participants, characterized by their preferences and behaviors, were identified using a clustering algorithm. Classification results provide utilities with insight into where resources and efforts should be allocated to users who are more likely to comply with DLC. Clustering users into different typologies will enable utilities to design targeted and less disruptive DLC better aligned with the needs of DLC participants.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".